

'''simple docstring'''

import os
from pickle import UnpicklingError
from typing import Dict, Tuple

import jax
import jax.numpy as jnp
import numpy as np
from flax.serialization import from_bytes
from flax.traverse_util import flatten_dict, unflatten_dict

import transformers

from .utils import logging


A_							    =				logging.get_logger(__name__)





def  A_	(			snake_case   ,							snake_case   ,							snake_case   ,							snake_case=False			):
  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        "Loading a PyTorch model in Flax, requires both PyTorch and Flax to be installed. Please see"
        " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
        " instructions."			)
    raise

  if not is_sharded:
    SCREAMING_SNAKE_CASE:str       =       os.path.abspath(snake_case			)
    logger.info(F'''Loading PyTorch weights from {pt_path}'''			)

    SCREAMING_SNAKE_CASE:Union[str, Any]       =       torch.load(snake_case   ,							map_location="cpu"			)
    logger.info(F'''PyTorch checkpoint contains {sum(t.numel() for t in pt_state_dict.values()			):,} parameters.'''			)

    SCREAMING_SNAKE_CASE:Optional[Any]       =       convert_pytorch_state_dict_to_flax(snake_case   ,							snake_case			)
  else:
    # model is sharded and pytorch_checkpoint_path already contains the list of .pt shard files
    SCREAMING_SNAKE_CASE:Optional[int]       =       convert_pytorch_sharded_state_dict_to_flax(snake_case   ,							snake_case			)
  return flax_state_dict





def  A_	(			snake_case   ,							snake_case   ,							snake_case   ,							snake_case   ,							):

  def is_key_or_prefix_key_in_dict(snake_case			) -> bool:
    return len(set(snake_case			) & {key, (model_prefix,) + key}			) > 0

  # layer norm
  SCREAMING_SNAKE_CASE:Optional[Any]       =       pt_tuple_key[:-1] + ("""scale""",)
  if pt_tuple_key[-1] in ["weight", "gamma"] and is_key_or_prefix_key_in_dict(snake_case			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer mean
  SCREAMING_SNAKE_CASE:Tuple       =       pt_tuple_key[:-1] + ("""mean""",)
  if pt_tuple_key[-1] == "running_mean" and not is_key_or_prefix_key_in_dict(snake_case			):
    return renamed_pt_tuple_key, pt_tensor

  # batch norm layer var
  SCREAMING_SNAKE_CASE:Optional[Any]       =       pt_tuple_key[:-1] + ("""var""",)
  if pt_tuple_key[-1] == "running_var" and not is_key_or_prefix_key_in_dict(snake_case			):
    return renamed_pt_tuple_key, pt_tensor

  # embedding
  SCREAMING_SNAKE_CASE:List[Any]       =       pt_tuple_key[:-1] + ("""embedding""",)
  if pt_tuple_key[-1] == "weight" and is_key_or_prefix_key_in_dict(snake_case			):
    return renamed_pt_tuple_key, pt_tensor

  # conv layer
  SCREAMING_SNAKE_CASE:Tuple       =       pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and pt_tensor.ndim == 4 and not is_key_or_prefix_key_in_dict(snake_case			):
    SCREAMING_SNAKE_CASE:Tuple       =       pt_tensor.transpose(2   ,							3   ,							1   ,							0			)
    return renamed_pt_tuple_key, pt_tensor

  # linear layer
  SCREAMING_SNAKE_CASE:str       =       pt_tuple_key[:-1] + ("""kernel""",)
  if pt_tuple_key[-1] == "weight" and not is_key_or_prefix_key_in_dict(snake_case			):
    SCREAMING_SNAKE_CASE:Dict       =       pt_tensor.T
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm weight
  SCREAMING_SNAKE_CASE:List[str]       =       pt_tuple_key[:-1] + ("""weight""",)
  if pt_tuple_key[-1] == "gamma":
    return renamed_pt_tuple_key, pt_tensor

  # old PyTorch layer norm bias
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       pt_tuple_key[:-1] + ("""bias""",)
  if pt_tuple_key[-1] == "beta":
    return renamed_pt_tuple_key, pt_tensor

  # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
  SCREAMING_SNAKE_CASE:Optional[Any]       =       None
  if pt_tuple_key[-3::2] == ("parametrizations", "original0"):
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       pt_tuple_key[-2] + """_g"""
  elif pt_tuple_key[-3::2] == ("parametrizations", "original1"):
    SCREAMING_SNAKE_CASE:Optional[int]       =       pt_tuple_key[-2] + """_v"""
  if name is not None:
    SCREAMING_SNAKE_CASE:List[Any]       =       pt_tuple_key[:-3] + (name,)
    return renamed_pt_tuple_key, pt_tensor

  return pt_tuple_key, pt_tensor





def  A_	(			snake_case   ,							snake_case			):
  # convert pytorch tensor to numpy
  SCREAMING_SNAKE_CASE:Optional[Any]       =       {k: v.numpy() for k, v in pt_state_dict.items()}

  SCREAMING_SNAKE_CASE:Dict       =       flax_model.base_model_prefix

  # use params dict if the model contains batch norm layers
  if "params" in flax_model.params:
    SCREAMING_SNAKE_CASE:int       =       flax_model.params["""params"""]
  else:
    SCREAMING_SNAKE_CASE:List[str]       =       flax_model.params
  SCREAMING_SNAKE_CASE:Tuple       =       flatten_dict(snake_case			)

  # add batch_stats keys,values to dict
  if "batch_stats" in flax_model.params:
    SCREAMING_SNAKE_CASE:Optional[Any]       =       flatten_dict(flax_model.params["batch_stats"]			)
    random_flax_state_dict.update(snake_case			)

  SCREAMING_SNAKE_CASE:Optional[int]       =       {}

  SCREAMING_SNAKE_CASE:Union[str, Any]       =       (model_prefix not in flax_model_params) and (
      model_prefix in {k.split("."			)[0] for k in pt_state_dict.keys()}
  )
  SCREAMING_SNAKE_CASE:Dict       =       (model_prefix in flax_model_params) and (
      model_prefix not in {k.split("."			)[0] for k in pt_state_dict.keys()}
  )

  # Need to change some parameters name to match Flax names
  for pt_key, pt_tensor in pt_state_dict.items():
    SCREAMING_SNAKE_CASE:List[Any]       =       tuple(pt_key.split("."			)			)

    # remove base model prefix if necessary
    SCREAMING_SNAKE_CASE:Optional[int]       =       pt_tuple_key[0] == model_prefix
    if load_model_with_head_into_base_model and has_base_model_prefix:
      SCREAMING_SNAKE_CASE:Tuple       =       pt_tuple_key[1:]

    # Correctly rename weight parameters
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       rename_key_and_reshape_tensor(
        snake_case   ,							snake_case   ,							snake_case   ,							snake_case			)

    # add model prefix if necessary
    SCREAMING_SNAKE_CASE:Any       =       (model_prefix,) + flax_key in random_flax_state_dict
    if load_base_model_into_model_with_head and require_base_model_prefix:
      SCREAMING_SNAKE_CASE:List[Any]       =       (model_prefix,) + flax_key

    if flax_key in random_flax_state_dict:
      if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
        raise ValueError(
            F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
            F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

        # add batch stats if the model contains batchnorm layers
    if "batch_stats" in flax_model.params:
      if "mean" in flax_key[-1] or "var" in flax_key[-1]:
        SCREAMING_SNAKE_CASE:List[str]       =       jnp.asarray(snake_case			)
        continue
      # remove num_batches_tracked key
      if "num_batches_tracked" in flax_key[-1]:
        flax_state_dict.pop(snake_case   ,							snake_case			)
        continue

      # also add unexpected weight so that warning is thrown
      SCREAMING_SNAKE_CASE:Dict       =       jnp.asarray(snake_case			)

    else:
      # also add unexpected weight so that warning is thrown
      SCREAMING_SNAKE_CASE:int       =       jnp.asarray(snake_case			)

  return unflatten_dict(snake_case			)





def  A_	(			snake_case   ,							snake_case			):
  import torch

  # Load the index
  SCREAMING_SNAKE_CASE:List[Any]       =       {}
  for shard_file in shard_filenames:
    # load using msgpack utils
    SCREAMING_SNAKE_CASE:List[str]       =       torch.load(snake_case			)
    SCREAMING_SNAKE_CASE:Optional[int]       =       {k: v.numpy() for k, v in pt_state_dict.items()}

    SCREAMING_SNAKE_CASE:List[str]       =       flax_model.base_model_prefix

    # use params dict if the model contains batch norm layers and then add batch_stats keys,values to dict
    if "batch_stats" in flax_model.params:
      SCREAMING_SNAKE_CASE:Union[str, Any]       =       flax_model.params["""params"""]

      SCREAMING_SNAKE_CASE:Any       =       flatten_dict(snake_case			)
      random_flax_state_dict.update(flatten_dict(flax_model.params["batch_stats"]			)			)
    else:
      SCREAMING_SNAKE_CASE:Union[str, Any]       =       flax_model.params
      SCREAMING_SNAKE_CASE:Optional[Any]       =       flatten_dict(snake_case			)

    SCREAMING_SNAKE_CASE:Tuple       =       (model_prefix not in flax_model_params) and (
        model_prefix in {k.split("."			)[0] for k in pt_state_dict.keys()}
    )
    SCREAMING_SNAKE_CASE:Dict       =       (model_prefix in flax_model_params) and (
        model_prefix not in {k.split("."			)[0] for k in pt_state_dict.keys()}
    )
    # Need to change some parameters name to match Flax names
    for pt_key, pt_tensor in pt_state_dict.items():
      SCREAMING_SNAKE_CASE:List[str]       =       tuple(pt_key.split("."			)			)

      # remove base model prefix if necessary
      SCREAMING_SNAKE_CASE:Dict       =       pt_tuple_key[0] == model_prefix
      if load_model_with_head_into_base_model and has_base_model_prefix:
        SCREAMING_SNAKE_CASE:Optional[int]       =       pt_tuple_key[1:]

      # Correctly rename weight parameters
      SCREAMING_SNAKE_CASE:int       =       rename_key_and_reshape_tensor(
          snake_case   ,							snake_case   ,							snake_case   ,							snake_case			)
      # add model prefix if necessary
      SCREAMING_SNAKE_CASE:List[str]       =       (model_prefix,) + flax_key in random_flax_state_dict
      if load_base_model_into_model_with_head and require_base_model_prefix:
        SCREAMING_SNAKE_CASE:Optional[Any]       =       (model_prefix,) + flax_key

      if flax_key in random_flax_state_dict:
        if flax_tensor.shape != random_flax_state_dict[flax_key].shape:
          raise ValueError(
              F'''PyTorch checkpoint seems to be incorrect. Weight {pt_key} was expected to be of shape '''
              F'''{random_flax_state_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)

            # add batch stats if the model contains batchnorm layers
      if "batch_stats" in flax_model.params:
        if "mean" in flax_key[-1]:
          SCREAMING_SNAKE_CASE:Dict       =       jnp.asarray(snake_case			)
          continue
        if "var" in flax_key[-1]:
          SCREAMING_SNAKE_CASE:Any       =       jnp.asarray(snake_case			)
          continue
        # remove num_batches_tracked key
        if "num_batches_tracked" in flax_key[-1]:
          flax_state_dict.pop(snake_case   ,							snake_case			)
          continue

        # also add unexpected weight so that warning is thrown
        SCREAMING_SNAKE_CASE:Optional[int]       =       jnp.asarray(snake_case			)

      else:
        # also add unexpected weight so that warning is thrown
        SCREAMING_SNAKE_CASE:Optional[Any]       =       jnp.asarray(snake_case			)
  return unflatten_dict(snake_case			)





def  A_	(			snake_case   ,							snake_case			):
  SCREAMING_SNAKE_CASE:List[Any]       =       os.path.abspath(snake_case			)
  logger.info(F'''Loading Flax weights from {flax_checkpoint_path}'''			)

  # import correct flax class
  SCREAMING_SNAKE_CASE:Dict       =       getattr(snake_case   ,							"Flax" + model.__class__.__name__			)

  # load flax weight dict
  with open(snake_case   ,							"rb"			) as state_f:
    try:
      SCREAMING_SNAKE_CASE:List[str]       =       from_bytes(snake_case   ,							state_f.read()			)
    except UnpicklingError:
      raise EnvironmentError(F'''Unable to convert {flax_checkpoint_path} to Flax deserializable object. '''			)

  return load_flax_weights_in_pytorch_model(snake_case   ,							snake_case			)





def  A_	(			snake_case   ,							snake_case			):

  try:
    import torch  # noqa: F401
  except ImportError:
    logger.error(
        "Loading a Flax weights in PyTorch, requires both PyTorch and Flax to be installed. Please see"
        " https://pytorch.org/ and https://flax.readthedocs.io/en/latest/installation.html for installation"
        " instructions."			)
    raise

  # check if we have bf16 weights
  SCREAMING_SNAKE_CASE:Tuple       =       flatten_dict(jax.tree_util.tree_map(lambda snake_case			: x.dtype == jnp.bfloataa   ,							snake_case			)			).values()
  if any(snake_case			):
    # convert all weights to fp32 if the are bf16 since torch.from_numpy can-not handle bf16
    # and bf16 is not fully supported in PT yet.
    logger.warning(
        "Found ``bfloat16`` weights in Flax model. Casting all ``bfloat16`` weights to ``float32`` "
        "before loading those in PyTorch model."			)
    SCREAMING_SNAKE_CASE:Tuple       =       jax.tree_util.tree_map(
        lambda snake_case			: params.astype(np.floataa			) if params.dtype == jnp.bfloataa else params   ,							snake_case			)

  SCREAMING_SNAKE_CASE:Dict       =       flatten_dict(snake_case			)
  SCREAMING_SNAKE_CASE:List[Any]       =       pt_model.state_dict()

  SCREAMING_SNAKE_CASE:Dict       =       (pt_model.base_model_prefix in flax_state) and (
      pt_model.base_model_prefix not in {k.split("."			)[0] for k in pt_model_dict.keys()}
  )
  SCREAMING_SNAKE_CASE:Optional[int]       =       (pt_model.base_model_prefix not in flax_state) and (
      pt_model.base_model_prefix in {k.split("."			)[0] for k in pt_model_dict.keys()}
  )

  # keep track of unexpected & missing keys
  SCREAMING_SNAKE_CASE:Tuple       =       []
  SCREAMING_SNAKE_CASE:Union[str, Any]       =       set(pt_model_dict.keys()			)

  for flax_key_tuple, flax_tensor in flax_state_dict.items():
    SCREAMING_SNAKE_CASE:Any       =       flax_key_tuple[0] == pt_model.base_model_prefix
    SCREAMING_SNAKE_CASE:List[Any]       =       """.""".join((pt_model.base_model_prefix,) + flax_key_tuple			) in pt_model_dict

    # adapt flax_key to prepare for loading from/to base model only
    if load_model_with_head_into_base_model and has_base_model_prefix:
      SCREAMING_SNAKE_CASE:List[Any]       =       flax_key_tuple[1:]
    elif load_base_model_into_model_with_head and require_base_model_prefix:
      SCREAMING_SNAKE_CASE:List[str]       =       (pt_model.base_model_prefix,) + flax_key_tuple

    # rename flax weights to PyTorch format
    if flax_key_tuple[-1] == "kernel" and flax_tensor.ndim == 4 and ".".join(snake_case			) not in pt_model_dict:
      # conv layer
      SCREAMING_SNAKE_CASE:Tuple       =       flax_key_tuple[:-1] + ("""weight""",)
      SCREAMING_SNAKE_CASE:str       =       jnp.transpose(snake_case   ,							(3, 2, 0, 1)			)
    elif flax_key_tuple[-1] == "kernel" and ".".join(snake_case			) not in pt_model_dict:
      # linear layer
      SCREAMING_SNAKE_CASE:Tuple       =       flax_key_tuple[:-1] + ("""weight""",)
      SCREAMING_SNAKE_CASE:Union[str, Any]       =       flax_tensor.T
    elif flax_key_tuple[-1] in ["scale", "embedding"]:
      SCREAMING_SNAKE_CASE:List[Any]       =       flax_key_tuple[:-1] + ("""weight""",)

    # adding batch stats from flax batch norm to pt
    elif "mean" in flax_key_tuple[-1]:
      SCREAMING_SNAKE_CASE:List[str]       =       flax_key_tuple[:-1] + ("""running_mean""",)
    elif "var" in flax_key_tuple[-1]:
      SCREAMING_SNAKE_CASE:Dict       =       flax_key_tuple[:-1] + ("""running_var""",)

    if "batch_stats" in flax_state:
      SCREAMING_SNAKE_CASE:int       =       """.""".join(flax_key_tuple[1:]			)  # Remove the params/batch_stats header
    else:
      SCREAMING_SNAKE_CASE:int       =       """.""".join(snake_case			)

    # We also need to look at `pt_model_dict` and see if there are keys requiring further transformation.
    SCREAMING_SNAKE_CASE:Union[str, Any]       =       {}
    # New `weight_norm` from https://github.com/huggingface/transformers/pull/24030
    for key in pt_model_dict:
      SCREAMING_SNAKE_CASE:int       =       key.split("."			)
      SCREAMING_SNAKE_CASE:Tuple       =       None
      if key_components[-3::2] == ["parametrizations", "original0"]:
        SCREAMING_SNAKE_CASE:List[str]       =       key_components[-2] + """_g"""
      elif key_components[-3::2] == ["parametrizations", "original1"]:
        SCREAMING_SNAKE_CASE:Tuple       =       key_components[-2] + """_v"""
      if name is not None:
        SCREAMING_SNAKE_CASE:Tuple       =       key_components[:-3] + [name]
        SCREAMING_SNAKE_CASE:Union[str, Any]       =       """.""".join(snake_case			)
        SCREAMING_SNAKE_CASE:Any       =       key

    if flax_key in special_pt_names:
      SCREAMING_SNAKE_CASE:Union[str, Any]       =       special_pt_names[flax_key]

    if flax_key in pt_model_dict:
      if flax_tensor.shape != pt_model_dict[flax_key].shape:
        raise ValueError(
            F'''Flax checkpoint seems to be incorrect. Weight {flax_key_tuple} was expected '''
            F'''to be of shape {pt_model_dict[flax_key].shape}, but is {flax_tensor.shape}.'''			)
      else:
        # add weight to pytorch dict
        SCREAMING_SNAKE_CASE:List[Any]       =       np.asarray(snake_case			) if not isinstance(snake_case   ,							np.ndarray			) else flax_tensor
        SCREAMING_SNAKE_CASE:Dict       =       torch.from_numpy(snake_case			)
        # remove from missing keys
        missing_keys.remove(snake_case			)
    else:
      # weight is not expected by PyTorch model
      unexpected_keys.append(snake_case			)

  pt_model.load_state_dict(snake_case			)

  # re-transform missing_keys to list
  SCREAMING_SNAKE_CASE:int       =       list(snake_case			)

  if len(snake_case			) > 0:
    logger.warning(
        "Some weights of the Flax model were not used when initializing the PyTorch model"
        F''' {pt_model.__class__.__name__}: {unexpected_keys}\n- This IS expected if you are initializing'''
        F''' {pt_model.__class__.__name__} from a Flax model trained on another task or with another architecture'''
        " (e.g. initializing a BertForSequenceClassification model from a FlaxBertForPreTraining model).\n- This"
        F''' IS NOT expected if you are initializing {pt_model.__class__.__name__} from a Flax model that you expect'''
        " to be exactly identical (e.g. initializing a BertForSequenceClassification model from a"
        " FlaxBertForSequenceClassification model)."			)
  else:
    logger.warning(F'''All Flax model weights were used when initializing {pt_model.__class__.__name__}.\n'''			)
  if len(snake_case			) > 0:
    logger.warning(
        F'''Some weights of {pt_model.__class__.__name__} were not initialized from the Flax model and are newly'''
        F''' initialized: {missing_keys}\nYou should probably TRAIN this model on a down-stream task to be able to'''
        " use it for predictions and inference."			)
  else:
    logger.warning(
        F'''All the weights of {pt_model.__class__.__name__} were initialized from the Flax model.\n'''
        "If your task is similar to the task the model of the checkpoint was trained on, "
        F'''you can already use {pt_model.__class__.__name__} for predictions without further training.'''			)

  return pt_model








def 							A__       (    lowerCamelCase = 4_00_00_00    )	->						int:
				UpperCamelCase_:				Dict						=    []
				UpperCamelCase_, UpperCamelCase_:				Optional[int]						=    0, 1
				while b <= n:
								if b % 2 == 0:
												even_fibs.append(lowerCamelCase    )
								UpperCamelCase_, UpperCamelCase_:				int						=    b, a + b
				return sum(lowerCamelCase    )


if __name__ == "__main__":
							print(F"""{solution() = }""")






